Paper Info
Title
Discovering Hidden Course Requirements and Student Competences from Grade Data.
Abstract
This paper presents a data driven approach to autonomous course-competency requirement and student-competency level discovery starting from the grades obtained by a sufficiently large set of students. The approach relies on collaborative filtering techniques, more precisely matrix decomposition, to derive the hidden competency requirements and levels that together should be responsible for observed grades. The discovered hidden features are translated into human understandable competencies by matching the computed values to expert input. The approach also allows for grade prediction for so far unobserved student course combinations, allowing for personalized study planning and student guidance. The technique is demonstrated on data from a \"Data Science and Knowledge Engineering\" Bachelor study, Maastricht University.
Year
DOI
Venue
2017
10.1145/3099023.3099034
UMAP (Adjunct Publication)
Field
DocType
Citations 
Recommender system,Competence (human resources),Collaborative filtering,Data-driven,Computer science,Matrix decomposition,Knowledge engineering,Artificial intelligence,Bachelor,Machine learning
Conference
0
PageRank 
References 
Authors
0.34
2
4
Name
Order
Citations
PageRank
Mara Houbraken100.34
Chang Sun200.34
Evgueni N. Smirnov32420.38
Kurt Driessens448934.75